Abstract
The main focus of this study was to illustrate the applicability of multiple correspondence analysis (MCA) in detecting and representing underlying structures in large datasets used to investigate cognitive ageing. Principal component analysis (PCA) was used to obtain main cognitive dimensions, and MCA was used to detect and explore relationships between cognitive, clinical, physical, and lifestyle variables. Two PCA dimensions were identified (general cognition/executive function and memory), and two MCA dimensions were retained. Poorer cognitive performance was associated with older age, less school years, unhealthier lifestyle indicators, and presence of pathology. The first MCA dimension indicated the clustering of general/executive function and lifestyle indicators and education, while the second association was between memory and clinical parameters and age. The clustering analysis with object scores method was used to identify groups sharing similar characteristics. The weaker cognitive clusters in terms of memory and executive function comprised individuals with characteristics contributing to a higher MCA dimensional mean score (age, less education, and presence of indicators of unhealthier lifestyle habits and/or clinical pathologies). MCA provided a powerful tool to explore complex ageing data, covering multiple and diverse variables, showing if a relationship exists and how variables are related, and offering statistical results that can be seen both analytically and visually.
Highlights
Analysis of research data requires unique considerations depending on the type of data collected and/or on the main purpose of the research
While in some cases data is collected in ordinal mode, often it is obtained in categorized groups
Multiple correspondence analysis (CA) (MCA) allows for the analysis of categorical or categorized variables encompassing more than two categorical variables [2,3,4,5,6,7]
Summary
Analysis of research data requires unique considerations depending on the type of data collected and/or on the main purpose of the research. As opposed to the traditional hypothesis testing designed to verify a priori hypotheses about relations between variables, exploratory data analysis is used to identify systematic relations between variables, when there are incomplete a priori expectations as to the nature of those relations. Falling in the latter category, the method correspondence analysis (CA), a (multivariate) descriptive data analytic technique, allows simplifying complex data and provides a detailed description of the data, yielding a simple, yet exhaustive analysis (a review of the development of the correspondence analysis methodology can be found in [1]). MCA is used to represent and model datasets as “clouds” of points in a multidimensional Euclidean space; this means that it is distinctive in describing
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
